Systems for automated HOV/HOT lane enforcement as well as for other additional enforcement (e.g., cell phone and seat belt violation detection) are being developed. Such systems rely on a camera-based imaging arrangement to capture images/videos in the near-infrared (NIR) band through a windshield or a side window of an oncoming vehicle. A detection algorithm examines the images/videos to determine candidate HOV/HOT violators for law-enforcement officers to take action on. Depending on the rules of the lane (e.g., 2 occupants or 3 occupants required), if the front and rear passenger seats are unoccupied, then the vehicle is classified as a candidate violator.
For a candidate violator, an evidence package consisting of a front seat image, a rear seat image, a license plate image, and a color vehicle image can be generated and sent to a downstream law enforcement officer or to a back office. In one workflow, a law enforcement officer may review the evidence package and determine whether the vehicle should be pulled over and the driver should be ticketed. In another workflow, the officer may be located in the back office at a police station and reviews the evidence package and determines whether the owner is ticketed through the mail. In another application, toll adjustments are made consistent with the number of occupants in the vehicle.
FIG. 1 illustrates a pair of prior art front and side view images 12, 14, respectively captured by a camera based HOV/HOT enforcement system. An algorithm is utilized to first localize the vehicle windshield and passenger side window from the captured front and side view images 12, 14, respectively. The localized regions are then passed into a classifier to identify if the vehicle has a passenger in the front and/or rear seat. Detecting windshield and side windows accurately is important in the system pipeline as the errors made in the localization step are propagated through the rest of the system. While the (front) windshield detection performance is typically higher than 99.9% in various weather, camera, and illumination conditions, achieving a similar performance for side window detection remains a challenge.
FIG. 2 illustrates a group 20 of images 22, 24, 26, 28, and 29 depicting variations in passenger side windows for different vehicle types. A fundamental reason for the performance gap between windshield and side window detectors as described above is due to the large variation of side window shapes for different vehicle types as illustrated in FIG. 2.